Statistical-prediction-based automatic cloud CDN (Content Delivery Network) resource automatic deployment method

Abstract

The invention discloses a statistical-prediction-based automatic cloud CDN (Content Delivery Network) resource deployment method which comprises the steps of: predicting a load prediction value in a next 24h according to history data of a load of each edge node of a CDN, formulating a virtual server resource deployment plan of a corresponding edge node, carrying out virtual server resource deployment on each edge node of a cloud CDN; monitoring a load condition of each edge node of the cloud CDN in real time, when the cloud CDN is in a user visit rush time, mounting mirror images configured with relevant businesses to virtual servers by a cloud platform, and starting so as to be added in each edge node of the cloud CDN for sharing burst visit pressure; and when the cloud CDN is in a non-visit rush time, only preserving the virtual server which can keep business operate at a normal level, and recovering idle virtual server resources to a resource pool. According to the statistical-prediction-based cloud CDN resource automatic deployment method, the burst business rush time can be effectively dealt, the resource utilization rate of the CDN is increased, and the energy consumption and the maintenance and operation cost of the CDN are lowered.

Description

technical field [0001] The invention relates to the communication field, in particular to a method for automatically deploying cloud CDN resources based on statistical prediction. Background technique [0002] CDN (Content Delivery Network) is a content distribution network, which sends content (video, audio, web pages, etc.) to the "edge" of the network closest to the user by adding a new network architecture to the existing IP bearer network. It allows users to obtain the content they need nearby, thereby optimizing network traffic conditions and improving the response speed of users visiting websites. CDN can solve the problems of slow content transmission and stability caused by the small bandwidth of the backbone network, the large number of user visits, and the uneven distribution of network points. [0003] However, the access volume of the CDN service is not balanced, and has strong periodicity and burstiness. For example, according to the data analysis of China Te...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of low utilization rate of existing CDN resources and poor elastic capacity expansion, the present invention provides an automatic deployment method of cloud CDN resources based on statistical prediction. Through load prediction and real-time monitoring of CDN edge nodes, CDN is realized On-demand resource allocation and automatic, rapid elastic expansion

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